Word embedding is one of the most popular continuous representation of textual data. These embeddings are vector representations of a particular word.
Among them, word2vec [1]} is one of the most used in Sentiment Analysis tasks such as Polarity or Emotion State Classification [2]}, [3]}.
Word2vec word embeddings are static: a word has always the same vector representation,
regardless of the true meaning of the word and the context of its occurrence.
This is problematic for polysemic words, for instance frequent in French [4]}.
